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1. Algorithmic Trading: What Is It and How Does It Work? Algorithmic trading is a type of trading that uses complex algorithms to determine the best time to buy or sell a stock or other financial instrument. Algorithmic trading systems use mathematical models and computer programs to make decisions about when to buy and sell. These systems can scan markets for potential opportunities, monitor news and events, and execute trades according to predetermined rules. Algorithmic trading has become increasingly popular as technology and computing power have advanced, making it possible to execute trades faster and with greater precision. Algorithmic trading can be used to help traders achieve better returns and reduce risk by eliminating emotions from the decision-making process.
Are you preparing for an Android Developer interview? Whether you’re facing algorithm challenges, technical questions, system design exercises, or behavioral interviews, this comprehensive guide will…
Visual and action data are interconnected in robotic tasks, forming a perception-action loop. Robots rely on control parameters for movement, while VFMs excel in processing visual data. However, a modality gap exists between visual and action data arising from the fundamental differences in their sensory modalities, abstraction levels, temporal dynamics, contextual dependence, and susceptibility to noise. These differences make it challenging to directly relate visual perception to action control, requiring intermediate representations or learning algorithms to bridge the gap. Currently, robots are represented by geometric primitives like triangle meshes, and kinematic structures describe their morphology. While VFMs provide generalizable control
The problem of over-optimization of likelihood in Direct Alignment Algorithms (DAAs), such as Direct Preference Optimisation (DPO) and Identity Preference Optimisation (IPO), arises when these methods fail to improve model performance despite increasing the likelihood of preferred outcomes. These algorithms, which are alternatives to Reinforcement Learning from Human Feedback (RLHF), aim to align language models with human preferences by directly optimizing for desired outcomes without explicit reward modeling. However, optimizing likelihood alone can sometimes degrade model performance, indicating a fundamental flaw in using likelihood as the primary alignment objective. Researchers from University College London and Cohere explore the issue of
I'm making an open source turn based JRPG in Godot 4.3, I want any programmer to be able to very easily create new enemies with different behaviors by simply customizing export variables in the edi...
Following my previous article on arrays, I want to discuss the linked list data structure and some of its operations. As data scientists, even though we are not necessarily expected to know data…
Discover a comprehensive roadmap for starting your AI journey. From identifying your specific AI use case to selecting the most suitable algorithm and achieving positive results, these 10 steps will guide you in developing an effective strategy to meet your business goals.
By Gabriel del Valle 10/08/24 www.linkedin.com/in/gabrielxdelvalle Introduction One of the most transformative ways that Data Science has brought value to major web platforms like Amazon and Spotify is modeling user preferences to make recommendations which the user finds relevant. For content driven platforms like TikTok or Meta, the quality of their recommendation algorithm is